One can aptly describe many of today’s organizations as complex adaptive systems. Dynamic interaction patterns and emergent relationships characterize their complexity, while their ability to change and self-organize capture their adaptiveness. To survive, traditional mechanistic, highly structured organizations characterized by rigid, vertical communications have transformed to organic, rapidly changing organizations that exhibit nearly amorphous communication patterns. Organizations that learn to adapt usually survive or even thrive; conversely, those that don’t adapt, either dissolve or become insignificant.
In September 2016 Cambridge Semantics attended Strata+Hadoop World 2016 in New York, NY. While we were there, Marty Loughlin, our VP of Financial Services, spoke to a gathering of attendees about who we are and what our platform does. Here is his presentation.
"So what?" you might say. Another hyperbole-fueled headline in tech is hardly a notable event. To answer, let's start with what we did.
Data lakes are quickly becoming a hot topic as enterprises determine how best to organize and access the large volume of data they have been generating. Data Lakes are attractive for several reasons, including their ability to expand data across the enterprise while maintaining trust and security with data governance.
Data transformation is one of the most vital facets of data management. Prior to integrating data sources, conducting analytics, or utilizing data in most operational applications, data must be transformed from its native state to one suitable for the target system—even if it’s just a data mart.
We are at an inflection point in the financial services industry. The evolving and overwhelming demands of regulatory compliance have forced organizations to acknowledge the need for data governance and most are developing their strategy.
State Street Bank, The EDM Council, Dun & Bradstreet, Wells Fargo and Cambridge Semantics completed an engagement to harmonize State Street's Interest Rate Swap data with Dun & Bradstreet's entity hierarchy data using the Financial Industry Business Ontology (FIBO) and Cambridge Semantics' Anzo Smart Data Lake®.
Exploratory analytics represents the evolution—and perhaps culmination—of conventional analytics and business intelligence options. It’s a combination of real-time data discovery and ad-hoc, graph-aware analytics that provides automatic, self-service insight for end users on all their data.
With no inherent means of adhering to governance and security protocols, data lakes are akin to the Wild West in that they are devoid of order and consistency. Each user manipulates his or her own data at the risk of the reuse of that data for others.
Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.